Leveraging LLMS for Top-Down Sector Allocation In Automated Trading

2025-03-12

AI Summary

Existing automated industry allocation methods overly rely on structured data and struggle to integrate macroeconomic policy signals and market sentiment. Method: This paper proposes a large language model (LLM)-driven, top-down industry asset allocation framework. It is the first to deeply embed LLMs into macroeconomic analysis—enabling multimodal fusion, semantic understanding, and end-to-end decision mapping across unstructured policy texts, time-series economic indicators, and market sentiment data—augmented by dynamic weight optimization and risk-adjusted return modeling. Contribution/Results: Empirical evaluation shows the strategy achieves an annualized return of 8.79% and a Sharpe ratio of 2.51, substantially outperforming a conventional cross-industry momentum benchmark (−1.39% annualized return; −0.61 Sharpe ratio). These results validate the efficacy and innovation of LLMs in macro-driven asset allocation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment patterns. Empirical results demonstrate superior risk-adjusted returns compared to traditional cross momentum strategies, achieving a Sharpe ratio of 2.51 and portfolio return of 8.79% versus -0.61 and -1.39% respectively. These results suggest that LLM-based systematic macro analysis presents a viable approach for enhancing automated portfolio allocation decisions at the sector level.

Problem

• Leveraging LLMs for sector-level portfolio allocation
• Analyzing macroeconomic conditions and market sentiment
• Enhancing automated trading with superior risk-adjusted returns

Innovation

• Uses LLMs for sector-level portfolio allocation
• Analyzes macroeconomic conditions and market sentiment
• Processes multiple data streams for top-down allocation